Application of different denoising methods and fractal theory in landslide deformation tendency prediction

In order to predict landslide deformation tendency accurately, firstly, we used a variety of denoising methods to denoise the landslide deformation data and separate the landslide deformation tendency data and error data, and took fractal theory to judge and compare the deformation tendency of each sequence. Secondly, in order to verify the judgment accuracy of the landslide deformation tendency, we used neural network to predict the landslide deformation. The results showed that the denoising effects by different denoising methods were quite different, the denoising effect of semi parametric Calman filtering was the most optimal, and the landslide has a tendency of greater deformation and worse stability; meanwhile, compared with the tendency judgment of deformation prediction results, the consistency was good, proving the accuracy of the landslide deformation tendency judgment. This study could provide a new idea for judging the landslide deformation tendency.